LLM-Based Exogenous World Modeling for Dynamic Scheduling
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
arXiv:2511. 02748v2 Announce Type: replace-cross Abstract: We argue that sixth-generation (6G) intelligence is not fluent token prediction but the capacity to imagine and choose -- to simulate future scenarios, weigh trade-offs, and act with calibrated uncertainty.
The paper introduces a dynamic multi‑step decoding approach for time‑series foundation models, arguing that the best decoding strategy varies by instance rather than being fixed. A lightweight selector network ranks candidate rollout schedules using the backbone’s hidden states, trained on historical windows and evaluated via dynamic programming to keep complexity quadratic. At deployment, multiple selectors vote to choose a schedule, defaulting to one‑shot decoding when they disagree, and the method shows statistically significant improvements in point‑forecast accuracy on the fev‑bench benchmark with frozen Chronos‑2 and Moirai‑1.1‑R‑small backbones.
Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate domain expertise, assess prediction plausibility, and...
arXiv:2608.30976v1 Announce Type: new Abstract: Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate doma...
arXiv:2609.13840v1 Announce Type: new Abstract: A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters ar...
CEDAR is a two‑stage framework for demand forecasting that incorporates planned actions and external event signals. Stage I uses an Action‑Interleaved Transformer to model controllable state transitions under interventions, while Stage II applies a Residual Correction Module that aligns event descriptions with product context using LLM‑assisted text representations. Experiments on a large Alibaba 1688 dataset show that CEDAR improves simulation accuracy over traditional time‑series forecasting baselines and benefits real‑world budget planning.